ShuffleFlow is a variational inference framework that partitions images with pixel-unshuffling and models the joint posterior over sub-images using a shared conditional normalizing flow conditioned on neural field features for scalable Bayesian inverse imaging.
Event-horizon-scale imaging of m87* under different assumptions via deep generative image priors,
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ShuffleFlow: Scalable Posterior Inference for Bayesian Inverse Imaging
ShuffleFlow is a variational inference framework that partitions images with pixel-unshuffling and models the joint posterior over sub-images using a shared conditional normalizing flow conditioned on neural field features for scalable Bayesian inverse imaging.